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Identification of Treatment Effects on the Treated with One-Sided Non-Compliance

Author

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  • Frölich, Markus

    (University of Mannheim)

  • Melly, Blaise

    (Brown University)

Abstract

Traditional instrumental variable estimators do not generally estimate effects for the treated population but for the unobserved population of compliers. They do identify effects for the treated when there is one-sided perfect non-compliance. However, this property is lost when covariates are included in the model. In this case, we show that the effects for the treated are still identified but require modified estimators. We consider both average and quantile treatment effects and allow the instrument to be discrete or continuous.

Suggested Citation

  • Frölich, Markus & Melly, Blaise, 2008. "Identification of Treatment Effects on the Treated with One-Sided Non-Compliance," IZA Discussion Papers 3671, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp3671
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    5. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    6. Peter M. Steiner & Yongnam Kim & Courtney E. Hall & Dan Su, 2017. "Graphical Models for Quasi-experimental Designs," Sociological Methods & Research, , vol. 46(2), pages 155-188, March.
    7. Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2010. "Inverse Propensity Score Weighted Estimation of Local Average Treatment Effects and a Test of the Unconfoundedness Assumption," CEU Working Papers 2012_9, Department of Economics, Central European University, revised 11 Aug 2010.
    8. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    9. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
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    11. Byeong Yeob Choi, 2024. "Instrumental variable estimation of weighted local average treatment effects," Statistical Papers, Springer, vol. 65(2), pages 737-770, April.
    12. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP77/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Carbuccia, Laudine & Heim, Arthur & Barone, Carlo & Chevallier, Coralie, 2024. "Investigating how administrative burden and search costs affect social inequalities in early childcare access, a randomised controlled trial," OSF Preprints w2ey7, Center for Open Science.
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    15. Phillip Heiler, 2022. "Efficient Covariate Balancing for the Local Average Treatment Effect," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1569-1582, October.

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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